The present invention relates to the recommendation of content items for users of communication equipments capable of being connected to at least one communication network, and more precisely to personalization of user content recommendations.
One means here by “user profile” a set of data (or metadata) describing the identity of a user, such as socio-demographic data and interests (or preferences or else habits) relative to one or more subjects (television programs, radio programs, news, music, advertising, entertainment, items to buy or use of services, for instance). (Meta)Data describing user interests can be words or expressions or else concepts that most of the time may be found also in the descriptions that are associated to the content items.
Moreover, one means here by “user content recommendations” recommendations (or suggestions) of content items that are proposed to a user based on interests (or preferences or else habits) contained into his profile.
More, one means here by “concept” something defined by one or more words or from one or several taxonomies or ontologies, and therefore associated to a semantic content. It is usually described by a set of at least one property (i.e. a set of descriptors having a type and a value).
One major challenge in the domain of personalization of user content recommendations is to understand the interests of users as properly as possible in order to provide them with content items suited to their respective needs.
As it is known by the man skilled in the art, usual techniques for content recommendation (such as content-based filtering algorithms) do not allow introducing in the user profiles concepts that did not appear in the descriptions of content that have been consumed by the users. Indeed, when a content item is consumed by a user, metadata that describe this content item can be added (according to various heuristics) to the user profile, and then recommendations are generated on the basis of similarities between the user profile and the content metadata. So, content items for which there are no corresponding metadata in a user profile will not be recommended to the user.
These approaches tend to overspecialise the recommendations and do not enable to add enough diversity/discovery in the recommendations. More precisely, these approaches do not allow anticipating user interests: the user must have consumed something described by a concept (or equivalent) to have further recommendations related to that concept. For example, with existing systems if a user liked one book of Victor Hugo, the recommender system will tend to recommend him other books of Victor Hugo, whereas this user (but also many others) may be interested in discovering other writers, just because his (their) taste(s) evolve(s) naturally with time and also because of the influence of the items he (they) has (have) consumed, and of other interests he (they) may have had in the meantime.
Other approaches have been recently proposed to improve the user content recommendations.
A first approach concerns the so-called “collaborative recommenders”. Typical collaborative recommenders comprise a core mechanism consisting in finding hidden links between users based on the similarity of their preferences or historic behaviour. Items are recommended to a certain user with respect to the interests he shares with other users or according to opinions, comparatives, and ratings of items given by other users having similar profiles.
So, in this first approach, the comparison between users and items is done globally, without taking into account the temporal dimension, and therefore this approach may be not very relevant when there is a strong dependency between two items. Moreover, even if collaborative recommenders allow to bring some diversity in the recommendations, they are not able to address directly the problem of anticipating the next (or future) user interests. More, collaborative recommenders require a significant number of user ratings in order to be effective (this is the so called “cold-start problem”).
A second approach concerns the so-called “frequent pattern mining algorithms”. These algorithms enable finding dependencies between the appearance of items in a set of transactions, as described notably in the article of Goethals, B., “Survey on frequent pattern mining”, Technical report, Helsinki Institute for Information Technology, 2003. The goal of a frequent pattern mining algorithm is to find a set of frequent and confident association rules of the form “XY”, which means: “if the set of items X appears in a transaction, then another set of items Y also appears in that transaction”. An association rule is considered as frequent if the ratio between the number of transactions in which X and Y appear together and the total number of transactions satisfies a frequency threshold. Similarly, an association rule is confident if the percentage of transactions where X and Y appear together among all transactions containing X is greater than a confidence threshold.
Even if frequent pattern mining algorithms enable finding relationship between items, they are only concerned with the presence of items in transactions, but they do not consider the moment of appearance of preferences (or items). There exist approaches for frequent temporal pattern mining (for example the one described in the article of Sivaselvan, B. et al, “An efficient frequent temporal pattern mining algorithm,” Information Technology Journal, vol. 5, pp. 1043-1047, 2006) which takes into account the order of appearance of items in transactions. However, the temporality is considered in terms of the number of items separating the two frequent sets of items and not in terms of the time elapsed between them.
A third approach concerns the so-called “temporal probabilistic models”. Some probabilistic models have been proposed for user interest and interest transition capturing and predicting. They have been notably described in the article of Suzuki, T. et al., “Learning to estimate user interest utilizing the Variational Bayes' estimator”, Proceedings of 5th International Conference on Intelligent Systems Design and Applications, Wroclaw, Poland, 94-99, 2005, and in the article of Wang, S. et al, “Mining Interest Navigation Patterns with Hidden Markov Model”, Lecture Notes in Computer Science Volume 4027/2006, pp. 470-478, Springer, 2000.
These last models have been conceived for single user interest transition capturing, but not for applying captured interest transition patterns of one user to similar users.